System for optimizing a patient&#39;s insulin dosage regimen

ABSTRACT

A system for optimizing a patient&#39;s insulin dosage regimen over time, comprising at least a first memory for storing data inputs corresponding at least to one or more components in a patient&#39;s present insulin dosage regimen, and data inputs corresponding at least to the patient&#39;s blood-glucose-level measurements determined at a plurality of times, and a processor operatively connected to the at least first memory. The processor is programmed at least to determine from the data inputs corresponding to the patient&#39;s blood-glucose-level measurements determined at a plurality of times whether and by how much to vary at least one of the one or more components in the patient&#39;s present insulin dosage regimen in order to maintain the patient&#39;s future blood-glucose-level measurements within a predefined range.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/417,960, filed 3 Apr. 2009, and claims the benefit of priority from,U.S. provisional Application Ser. No. 61/042,487, filed 4 Apr. 2008, andU.S. provisional Application Ser. No. 61/060,645, filed 11 Jun. 2008.Each of these applications are incorporated herein by reference in theirentireties.

FIELD OF THE INVENTION

The present invention relates to a system for optimizing the insulindosage regimen for a diabetes patient, and more particularly to such asystem according to which a processor is programmed at least todetermine from the data inputs corresponding to the patient'sblood-glucose-level measurements determined at a plurality of timeswhether and by how much to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen in order tomaintain the patient's future blood-glucose-level measurements within apredefined range.

BACKGROUND

Diabetes is a chronic disease resulting from deficient insulin secretionby the endocrine pancreas. About 7% of the general population in theWestern Hemisphere suffers from diabetes. Of these persons, roughly 90%suffer from Type-2 diabetes while approximately 10% suffer from Type-1.In Type-1 diabetes, patients effectively surrender their endocrinepancreas to autoimmune distraction and so become dependent on dailyinsulin injections to control blood-glucose-levels. In Type-2 diabetes,on the other hand, the endocrine pancreas gradually fails to satisfyincreased insulin demands, thus requiring the patient to compensate witha regime of oral medications or insulin therapy. In the case of eitherType-1 or Type-2 diabetes, the failure to properly control glucoselevels in the patient may lead to such complications as heart attacks,strokes, blindness, renal failure, and even premature death.

Insulin therapy is the mainstay of Type-1 diabetes management and one ofthe most widespread treatments in Type-2 diabetes, about 27% of thesufferers of which require insulin. Insulin administration is designedto imitate physiological insulin secretion by introducing two classes ofinsulin into the patient's body: Long-acting insulin, which fulfillsbasal metabolic needs; and short-acting insulin (also known asfact-acting insulin), which compensates for sharp elevations inblood-glucose-levels following patient meals. Orchestrating the processof dosing these two types of insulin, in whatever form (e.g., separatelyor as premixed insulin) involves numerous considerations.

First, patients measure their blood-glucose-levels (using some form of aglucose meter) on average about 3 to 4 times per day. The number of suchmeasurements and the variations therebetween complicates theinterpretation of these data, making it difficult to extrapolate trendstherefrom that may be employed to better maintain the disease. Second,the complexity of human physiology continuously imposes changes ininsulin needs for which frequent insulin dosage regimen adjustments arewarranted. Presently, these considerations are handled by a patient'sendocrinologist or other healthcare professional during clinicappointments. Unfortunately, these visits are relativelyinfrequent—occurring once every 3 to 6 months—and of short duration, sothat the physician or other healthcare professional is typically onlyable to review the very latest patient medical data. In consequence, ithas been shown that more than 60% of patients control their diabetes atsub-optimal levels, leading to unwanted complications from the disease.

Indeed, one of the major obstacles of diabetes management is the lack ofavailability of a patient's healthcare professional and the relativeinfrequency of clinic appointments. Studies have, in fact, establishedthat more frequent insulin dosage regimen adjustments—e.g., every 1 to 2weeks—improves diabetes control in most patients. Yet as the number ofdiabetes sufferers continues to expand, it is expected that thepossibility of more frequent insulin dosage regimen adjustments viaincreased clinic visits will, in fact, decrease. And, unfortunately,conventional diabetes treatment solutions do not address this obstacle.

The device most commonly employed in diabetes management is the glucosemeter. Such devices come in a variety of forms, although all arecharacterized by their ability to provide patients near instantaneousreadings of their blood-glucose-levels. This additional information canbe used to better identify dynamic trends in blood-glucose-levels.However, all conventional glucose meters are designed to be diagnostictools rather than therapeutic ones. Therefore, by themselves, evenstate-of-the-art glucose meters do not lead to improved glycemiccontrol.

One conventional solution to the treatment of diabetes is the insulinpump. Insulin pumps are devices that continuously infuse short actinginsulin into a patient at a predetermined rate to cover both basal needsand meals. As with manual insulin administration therapy, a healthcareprofessional sets the pump with the patient's insulin dosage regimenduring clinic visits. In addition to their considerable current expense,which prohibits their widespread use by patients with Type-2 diabetes,insulin pumps require frequent adjustment by the physician or otherhealthcare professional to compensate for the needs of individualpatients based upon frequent blood-glucose-level measurements.

An even more recent solution to diabetes treatment seeks to combine aninsulin pump and near-continuous glucose monitoring in an effort tocreate, in effect, an artificial pancreas regulating a patient'sblood-glucose-level with infusions of short-acting insulin. According tothis solution, real-time patient information is employed to matchinsulin dosing to the patient's dynamic insulin needs irrespective ofany underlying physician-prescribed treatment plan. While such systemsaddress present dosing requirements, they are entirely reactive and notinstantaneously effective. In consequence of these drawbacks, suchcombined systems are not always effective at controlling blood glucoselevels. For instance, such combined units cannot forecast unplannedactivities, such as exercise, that may excessively lower a patient'sblood-glucose level. And when the hypoglycemic condition is detected,the delay in the effectiveness of the insulin occasioned not only by thenature of conventional synthetic insulin but also the sub-dermaldelivery of that insulin by conventional pumps results in inefficientcorrection of the hypoglycemic event.

While the foregoing solutions are beneficial in the management andtreatment of diabetes in some patients, or at least hold the promise ofbeing so, there continues to exist the need for means that wouldcost-effectively improve diabetes control in patients.

SUMMARY OF THE INVENTION

According to the specification, there are disclosed several embodimentsof a system for optimizing a patient's insulin dosage regimen over time.In one embodiment, the system comprises at least a first memory forstoring data inputs corresponding at least to one or more components ofa patient's present insulin dosage regimen, and data inputscorresponding at least to the patient's blood-glucose-level measurementsdetermined at a plurality of times; and a processor operativelyconnected to the at least first memory. The processor is programmed atleast to determine from the data inputs corresponding to the patient'sblood-glucose-level measurements determined at a plurality of timeswhether and by how much to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen in order tomaintain the patient's future blood-glucose-level measurements within apredefined range.

In one embodiment, the at least first memory and the processor areresident in a single apparatus. Per one feature, the single apparatusfurther comprises a glucose meter. The glucose meter may be separatefrom the single apparatus, further to which the glucose meter is adaptedto communicate to the at least first memory of the single apparatus thedata inputs corresponding at least to the patient's blood-glucose-levelmeasurements determined at a plurality of times.

Per one feature thereof, the single apparatus may further comprises dataentry means for entering data inputs corresponding at least to thepatient's blood-glucose-level measurements determined at a plurality oftimes directly into the at least first memory.

There may, per another aspect of the invention, further be provided dataentry means disposed at a location remote from the single apparatus forremotely entering data inputs corresponding at least to the one or morecomponents in the patient's present insulin dosage regimen into the atleast first memory.

On one embodiment, the invention may comprise at least first data entrymeans disposed at a location remote from the at least first memory andprocessor for remotely entering data inputs corresponding at least tothe one or more components in the patient's present insulin dosageregimen into the at least first memory, and at least second data entrymeans, disposed at a location remote from the at least first memory,processor and at least first data entry means, for remotely enteringdata inputs corresponding at least to the patient's blood-glucose-levelmeasurements determined at a plurality of times into the at least firstmemory.

Per one aspect of the invention, the data inputs corresponding at leastto the patient's blood-glucose-level measurements determined at aplurality of times are each associated with an identifier indicative ofwhen the measurement was input into the memory. Optionally, there may beprovided data entry means enabling a user to define the identifierassociated with each blood-glucose-level measurement data-input, toconfirm the correctness of the identifier associated with eachblood-glucose-level measurement data-input, and/or to modify theidentifier associated with each blood-glucose-level measurementdata-input.

According to a still further feature, the processor is programmed todetermine on a predefined schedule whether and by how much to vary atleast one of the one or more components in the patient's present insulindosage regimen.

Per yet another feature of the invention, the processor is programmed todetermine whether each data input corresponding to the patient'sblood-glucose-level measurements represents a severe hypoglycemic event,and to vary at least one of the one or more components in the patient'spresent insulin dosage regimen in response to a determination that adata input corresponding to the patient's blood-glucose-levelmeasurements represents a severe hypoglycemic event.

According to yet another feature, the processor is programmed todetermine from the data inputs corresponding to the patient'sblood-glucose-level measurements determined at a plurality of times ifthere have been an excessive number of hypoglycemic events over apredefined period of time, and to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen in responseto a determination that there have been an excessive number of suchhypoglycemic events over a predefined period of time.

Per still another feature, the processor is programmed to determine fromthe data inputs corresponding at least to the patient'sblood-glucose-level measurements determined at a plurality of times ifthe patient's blood-glucose level measurements fall within or outside ofa predefined range, and to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen only if thepatient's blood-glucose level measurements fall outside of thepredefined range. The processor may be further programmed to determinefrom the data inputs corresponding at least to the patient'sblood-glucose-level measurements determined at a plurality of timeswhether the patient's blood-glucose-level measurements determined at aplurality of times represent a normal or abnormal distribution. In oneaspect, this determination comprises determining whether the thirdmoment of the distribution of the patient's blood-glucose-levelmeasurements determined at a plurality of times fall within a predefinedrange.

According to a further aspect of the invention, where the one or morecomponents in the patient's present insulin dosage regimen comprise along-acting insulin dosage component; the processor is programmed todetermine from the identifier indicative of when a measurement was inputinto the memory at least whether the measurement is a morning orbed-time blood-glucose-level measurement, to determine whether thepatient's morning and bed-time blood-glucose-level measurements fallwithin a predefined range, and to determine by how much to vary thepatient's long-acting insulin dosage component only when the patient'smorning and bed-time blood-glucose-level measurements are determined tofall outside of the said predefined range. In connection therewith, theprocessor may further be programmed to factor in an insulin sensitivitycorrection factor that defines both the percentage by which any of theone or more components of the insulin dosage regimen may be varied andthe direction in which any fractional variations in any of the one ormore components are rounded to the nearest whole number. Optionally, theat least first memory further stores data inputs corresponding to apatient's present weight, and the insulin sensitivity correction factoris in part determined from the patient's present weight. Per this aspectof the invention, the determination of by how much to vary thelong-acting insulin dosage component of a patient's present insulindosage regimen may be a function of the present long-acting insulindosage, the insulin sensitivity correction factor, and the patient'sblood-glucose-level measurements.

In another aspect of the invention, the one or more components in thepatient's present insulin dosage regimen comprise a short-acting insulindosage component defined by a carbohydrate ratio and plasma glucosecorrection factor, and the processor is programmed to determine whetherand by how much to vary the patient's carbohydrate ratio and plasmaglucose correction factor. In connection with this determination, theprocessor may be programmed to factor in an insulin sensitivitycorrection factor that defines both the percentage by which any one ormore components of the insulin dosage regimen may be varied and thedirection in which any fractional variations in the one or morecomponents are rounded to the nearest whole number.

Per one aspect of the invention, the determination of by how much tovary the present plasma glucose correction factor component of apatient's insulin dosage regimen may be a function of a predefined valuedivided by the mean of the total daily dosage of insulin administered tothe patient, the patient's present plasma glucose correction factor, andthe insulin sensitivity correction factor. Alternatively, a valuerepresenting twice the patient's daily dosage of long-acting insulin inthe present insulin dosage regimen may be substituted for the mean ofthe total daily dosage of insulin administered to the patient as anapproximation thereof. Per still another feature hereof, the plasmaglucose correction factor component of the patient's insulin dosageregimen may be quantized to predefined steps of mg/dL.

According to yet another feature of the invention, the determination ofby how much to vary the present carbohydrate ratio component of apatients insulin dosage regimen is a function of a predefined valuedivided by the mean of the total daily dosage of insulin administered tothe patient, the patient's present carbohydrate ratio, and the insulinsensitivity correction factor. Alternatively, a value representing twicethe patient's daily dosage of long-acting insulin in the present insulindosage regimen is substituted for the mean of the total daily dosage ofinsulin administered to the patient as an approximation thereof. Furtherhereto, the processor may also be programmed to determine a correctionfactor that allows variations to the carbohydrate ratio component of apatient's insulin dosage regimen to be altered in order to compensatefor a patient's individual response to insulin at different times of theday.

Per a still further feature of the invention, the one or more componentsin the patient's present insulin dosage regimen comprise a long-actinginsulin dosage component, and the determination of by how much to varythe long-acting insulin dosage component is constrained to an amount ofvariation within predefined limits.

According to yet another feature, the one or more components in thepatient's present insulin dosage regimen comprise a short-acting insulindosage component defined by a carbohydrate ratio and plasma glucosecorrection factor, and the determination of by how much to vary any oneor more of each component in the short-acting insulin dosage isconstrained to an amount of variation within predefined limits.

According, to a further feature, the one or more components in thepatient's present insulin dosage regimen comprise a short-acting insulindosage component taken according to a sliding scale, and the processoris programmed to determine whether and by how much to vary at least thesliding scale in order to maintain the patient's futureblood-glucose-level measurements within a predefined range. Thedetermination of by how much to vary the sliding scale may further beconstrained to an amount of variation within predefined limits.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the present invention and to show moreclearly how it may be carried into effect, reference will now be made,by way of example, to the accompanying drawings, which show exemplaryembodiments of the present invention, and in which:

FIG. 1 is a simplified schematic of an apparatus according to a firstexemplary embodiment of the invention;

FIG. 2 is a drawing of a representative display for providinginformation to a patient;

FIG. 3 is a drawing of another representative display for providinginformation to a patient;

FIG. 4 is a drawing yet another representative display for providinginformation to a patient;

FIG. 5 is a drawing of still another representative display forproviding information to a patient;

FIG. 6 is a simplified diagram of the an apparatus for employing theinventive system, according to a further embodiment thereof;

FIG. 7 is a simplified diagram of an apparatus for employing theinventive system, according to a further embodiment thereof;

FIG. 8 is a simplified diagram of an apparatus for employing theinventive system, according to a further embodiment thereof;

FIG. 9 is a schematic view of an exemplary arrangement for employing thepresent invention;

FIG. 10 is a schematic view of a second exemplary arrangement foremploying the present invention;

FIG. 11 is a generalized diagram of the steps employed in updating apatient's insulin dosage regimen according to an exemplary embodiment;and

FIG. 12 is a flowchart of the exemplary algorithm employed in updating apatient's insulin dosage regimen according to an exemplary embodiment.

DETAILED DESCRIPTION

As required, detailed descriptions of exemplary embodiments of thepresent invention are disclosed herein. However, it is to be understoodthat the disclosed embodiments are merely exemplary of the invention,which may be embodied in various and alternative forms. The accompanyingdrawings are not necessarily to scale, and some features may beexaggerated or minimized to show details of particular components.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a providing arepresentative basis for teaching one skilled in the art to variouslyemploy the present invention.

Turning now to the drawings, wherein like numerals refer to like orcorresponding parts throughout the several views, the present inventioncomprehends a system for optimizing the insulin dosage regimen Indiabetes patients over time—such as in between clinic visits—to therebyenhance diabetes control.

As used herein, the term “insulin dose” means and refers to the quantityof insulin taken on any single occasion, while the term “insulin dosageregimen” refers to and means the set of instructions (typically definedby the patient's physician or other healthcare professional) definingwhen and how much insulin to take in a given period of time and/or undercertain conditions. One conventional insulin dosage regimen comprisesseveral components, including a long-acting insulin dosage component, aplasma glucose correction factor component, and a carbohydrate ratiocomponent. Thus, for instance, an exemplary insulin dosage regimen for apatient might be as follows: 25 units of long acting insulin at bedtime;1 unit of fast-acting insulin for every 10 grams of ingestedcarbohydrates; and 1 unit of fast-acting insulin for every 20 mg/dL bywhich a patient's blood glucose reading exceeds 120 mg/dL.

Referring to FIG. 1, which constitutes a generalized schematic thereof,the invention according to an exemplary embodiment more particularlycomprises an apparatus 1 having at least a first memory 10 for storingdata inputs corresponding at least to one or more components of apatient's present insulin dosage regimen (whether comprising separateunits of long-acting and short-acting insulin, premixed insulin, etc.)and the patient's blood-glucose-level measurements determined at aplurality of times, a processor 20 operatively connected (indicated atline 11) to the at least first memory 10, and a display 30 operativelycoupled (indicated at line 31) to the processor and operative to displayat least information corresponding to the patient's present insulindosage regimen. The processor 20 is programmed at least to determinefrom the data inputs corresponding to the patient's blood-glucose-levelmeasurements determined at a plurality of times whether and by how muchto vary at least one or the one or more components of the patient'spresent insulin dosage regimen in order to maintain the patient's futureblood-glucose-level measurements within a predefined range. Suchvariation, if effected, leads to a modification of the patient's presentinsulin dosage regimen data as stored in the memory 10, as explainedfurther herein. Thus, the data inputs corresponding to the one or morecomponents of the patient's present insulin dosage regimen as stored inthe memory device 10 will, at a starting time for employment of theinventive apparatus, constitute an insulin dosage regimen prescribed bya healthcare professional, but those data inputs may subsequently bevaried by operation of the apparatus (such as during the time intervalbetween a patient's clinic visits). In the foregoing manner, theinventive apparatus is operative to monitor relevant patient data witheach new input of information (such as, at a minimum, the patient'sblood-glucose-level measurements), thereby facilitating the optimizationof the patient's insulin dosage regimen in between clinic visits.

It is contemplated that the apparatus as generalized above may beembodied in any of a variety of forms, including a purpose-built,PDA-like unit, a commercially available device such as a cell-phone,IPHONE, etc. Preferably, though not necessarily, such a device wouldinclude data entry means, such as a keypad, touch-screen interface, etc.(indicated generally at the dashed box 40) for the initial input by ahealthcare professional of data corresponding at least to a patient'spresent insulin dosage regimen (and, optionally, such additional datainputs as, for instance, the patient's present weight, defined upper andlower preferred limits for the patient's blood-glucose-levelmeasurements, etc.), as well as the subsequent data inputs correspondingat least to the patient's blood-glucose-level measurements determined ata plurality of times (and, optionally, such additional data inputs as,for instance, the patient's present weight, the number of insulin unitsadministered by the patient, data corresponding to when the patienteats, the carbohydrate content of the foodstuffs eaten, the meal type(e.g., breakfast, lunch, dinner, snack, etc.). As shown, such data entrymeans 40 are operatively connected (indicated at line 41) to the memory10.

Display 30 is operative to provide a visual display to the patient,healthcare professional, etc. of pertinent information, including, byway of non-limiting example, information corresponding to the presentinsulin dosage regimen for the patient, the current insulin dose (i.e.,number of insulin units the patient needs to administer on the basis ofthe latest blood-glucose-level measurement and current insulin dosageregimen), etc. To that end, display 30 is operatively connected to theprocessor 20, as indicated by the dashed line 31.

As noted, the data entry means 40 may take the form of a touch-screen,in which case the data entry means 40 and display 30 may be combined(such as exemplified by the commercially available IPHONE (Apple, Inc.,California)).

Referring then to FIGS. 2 through 5, there are depicted representativeimages for a display 30 and a touch-screen type, combined display30/data entry means 40 exemplifying both the patient information thatmay be provided via the display, as well as the manner of data entry.

More particularly, FIG. 2 shows a display 30 providing current date/timeinformation 32 as well as the patient's current blood-glucose-levelmeasurement 33 based upon a concurrent entry of that data. FIG. 2further depicts a pair of scrolling arrows 42 by which the patient isable to scroll through a list 34 of predefined choices representing thetime of the patient's said current blood-glucose-level measurement. Asexplained further herebelow in association with a description of anexemplary algorithm for implementing the invention, selection of one ofthese choices will permit the processor to associate the measurementdata with the appropriate measurement time for more precise control ofthe patient's insulin dosage regimen.

FIG. 3 shows a display 30 providing current date/time information 32, aswell as the presently recommended dose of short-acting insulin units35—based upon the presently defined insulin dosage regimen—for thepatient to take at lunchtime.

FIG. 4 shows a display 30 providing current date/time information 32, aswell as, according to a conventional “carbohydrate-counting” therapy,the presently recommended base (3 IUs) and additional doses (1 IU perevery 8 grams of carbohydrates ingested) of short-acting insulin units36 for the patient to take at lunchtime—all based upon the presentlydefined insulin dosage regimen.

In FIG. 5, there is shown a display 30 providing current date/timeinformation 32, as well as the presently recommended dose ofshort-acting insulin units 37—based upon the presently defined insulindosage regimen—for the patient to take at lunchtime according to adesignated amount of carbohydrates to be ingested. As further depictedin FIG. 5, a pair of scrolling arrows 42 are displayed, by which thepatient is able to scroll through a list of predefined meal choices 38,each of which will have associated therewith in the memory a number(e.g., grams) of carbohydrates. When the patient selects a meal choice,the processor is able to determine from the number of carbohydratesassociated with that meal, and the presently defined insulin dosageregimen, a recommended dose of short-acting insulin for the patient totake (in this example, 22 IUs of short-acting insulin for a lunch ofsteak and pasta).

In one embodiment thereof, shown in FIG. 6, the inventive apparatus asdescribed above in respect of FIG. 1 optionally includes a glucose meter(indicated by the dashed box 50) operatively connected (as indicated atline 51) to memory 10 to facilitate the automatic input of datacorresponding to the patient's blood-glucose-level measurements directlyto the memory 10.

Alternatively, it is contemplated that the glucose meter 50′ could beprovided as a separate unit that is capable of communicating (such asvia a cable or wirelessly, represented at line 51′) with the device 1′so as to download to the memory 10′ the patient's blood-glucose-levelmeasurements, such as shown in FIG. 7.

According to another embodiment, shown in FIG. 8, the inventiveapparatus 1″ may be combined with an insulin pump 60″ and, optionally, aglucose meter 50″ as well. According to this embodiment, the processor20″ is operative to determine from at least the patient'sblood-glucose-level measurement data (which may be automaticallytransferred to the memory 10″ where the apparatus is provided with aglucose meter 50″, as shown, is connectable to a glucose meter so thatthese data may be automatically downloaded to the memory 10″, or isprovided with data entry means 40″ so that these data may be input bythe patient) whether and by how much to vary the patient's presentinsulin dosage regimen in order to maintain the patient's futureblood-glucose-level measurements within a predefined range. Theprocessor 20″, which is operatively connected to the insulin pump 60″(indicated at line 61″), is operative to employ the insulin dosageregimen information to control the insulin units provided to the patientvia the pump 60″. Therefore, the processor 20″ and the pump 60″ form asemi-automatic, closed-loop system operative to automatically adjust thepump's infusion rate and profile based on at least the patient'sblood-glucose-level measurements. This will relieve the burden of havingto go to the healthcare provider to adjust the insulin pump's infusionrate and profile, as is conventionally the case. It will be appreciatedthat, further to this embodiment, the insulin pump 60″ may be operativeto transfer to the memory 10″ data corresponding to the rate at whichinsulin is delivered to the patient by the pump according to thepatient's present insulin dosage regimen. These data may be accessed bythe processor 20″ to calculate, for example, the amount of insulin unitsdelivered by the pump to the patient over a predefined period of time(e.g., 24 hours). Such data may thus be employed in the presentinvention to more accurately determine a patient's insulin sensitivity,plasma glucose correction factor and carbohydrate ratio, for instance.

Also further to this embodiment, the apparatus 1″ may optionally beprovided with data entry means, such as a keypad, touch-screeninterface, etc. (indicated generally at the dashed box 40″) for entry ofvarious data, including, for instance, the initial input by a healthcareprofessional of data corresponding at least to a patient's presentinsulin dosage regimen (and, optionally, such additional data inputs as,for instance, the patient's present weight, defined upper and lowerpreferred limits for the patient's blood-glucose-level measurements,etc.), as well as the subsequent data inputs corresponding at least tothe patient's blood-glucose-level measurements determined at a pluralityof times (to the extent that this information is not automaticallytransferred to the memory 10″ from the blood glucose meter 50″) and,optionally, such additional data inputs as, for instance, the patient'spresent weight, the number of insulin units administered by the patient,data corresponding to when the patient eats, the carbohydrate content ofthe foodstuffs eaten, the meal type (e.g., breakfast, lunch, dinner,snack), etc.

It is also contemplated that the invention may be effected through theinput of data by persons (e.g., patient and healthcare professional) atdisparate locations, such as illustrated in FIG. 9. For instance, it iscontemplated that the data inputs pertaining to at least the patient'sinitial insulin dosage regimen may be entered by the healthcareprofessional at a first location, in the form of a general purposecomputer, cell phone, IPHONE, or other device 100 (a general purposecomputer is depicted), while the subsequent data inputs (e.g., patientblood-glucose-level readings) may be entered by the patient at a secondlocation, also in the form of a general purpose computer, cell phone,IPHONE, or other device 200 (a general purpose computer is depicted),and these data communicated to a third location, in the form of acomputer 300 comprising the at least first memory and the processor.According to this embodiment, the computers 100, 200, 300 may benetworked in any known manner (including, for instance, via theinternet). Such networking is shown diagrammatically via lines 101 and201. Thus, for instance, the inventive system may be implemented via ahealthcare professional/patient accessible website through whichrelevant data are input and information respecting any updates to thepredefined treatment plan are communicated to the patient and healthcareprofessional.

Alternatively, it is contemplated that the invention may be effectedthrough the input of data via persons (e.g., patient and healthcareprofessional) at disparate locations, and wherein further one of thepersons, such as, in the illustrated example, the patient, is inpossession of a single device 200′ comprising the processor and memorycomponents, that device 200′ being adapted to receive data inputs from aperson at a disparate location. FIG. 10. This device 200′ could take anyform, including a general-purpose computer (such as illustrated), a PDA,cell-phone, purpose-built device such as heretofore described, etc.According to this embodiment, it is contemplated that the data inputspertaining to at least the patient's initial insulin dosage may beentered (for instance by the healthcare professional) at anotherlocation, such as via a general purpose computer, cell phone, or otherdevice 100′ (a general purpose computer is depicted) operative totransmit data to the device 200′, while the subsequent data inputs(e.g., patient blood-glucose-level measurements) may be entered directlyinto the device 200′. According to this embodiment, a healthcareprofessional could remotely input the patient's initial insulin dosageat a first location via the device 100′, and that data could then betransmitted to the patient's device 200′ where it would be received andstored in the memory thereof. According to a further permutation of thisembodiment, the aforedescribed arrangement could also be reversed, suchthat the patient data inputs (e.g., patient blood-glucose-levelmeasurements) may be entered remotely, such as via a cell phone,computer, etc., at a first location and then transmitted to a remotelysituated device comprising the processor and memory components operativeto determine whether and by how much to vary the patient's presentinsulin dosage regimen. According to this further permutation,modifications to the patient's insulin dosage effected by operation ofthe invention could be transmitted back to the patient via the same, oralternate, means.

Referring again to FIG. 9, it is further contemplated that there may beprovided a glucose meter 50′″ (including, for instance, in the form ofthe device as described above in reference to FIG. 6) that can interface51′″ (wirelessly, via a hard-wire connection such as a USB cable,FIREWIRE cable, etc.) with a general purpose computer 200 at thepatient's location to download blood-glucose-level measurements fortransmission to the computer 300 at the third location. Referring alsoto FIG. 10, it is further contemplated that this glucose meter 50′″ maybe adapted to interface 51′″ (wirelessly, via a hard-wire connectionsuch as a USB cable, FIREWIRE cable, etc.) with the single device 200′,thereby downloading blood-glucose-level measurement data to that devicedirectly.

Turning now to FIG. 11, there is shown a diagram generalizing the mannerin which the invention may be implemented to optimize a diabetespatient's insulin dosage regimen.

It will be understood that, in operation of the Invention according toany of the several embodiments as described herein, there is initiallyspecified, such as by a healthcare professional, a patient insulindosage regimen (comprised of, for instance, a carbohydrate ratio(“CHR”), a long-acting insulin dose, and a plasma glucose correctionfactor). Alternatively, the initial insulin dosage regimen can bespecified using published protocols for the initiation of insulintherapy, such as, for example, the protocols published by the AmericanDiabetes Association on Oct. 22, 2008. However specified, this insulindosage regimen data is entered in the memory of an apparatus (includingaccording to any of the several embodiment described above), such as bya healthcare professional, in the first instance and before the patienthas made any use of the apparatus.

Thereafter, the patient will input, or there will otherwiseautomatically be input (such as by the glucose meter) into the memory atleast data corresponding to each successive one of the patient'sblood-glucose-level measurements. Upon the input of such data, theprocessor determines, such as via the algorithm described herein,whether and by how much to vary the patient's present insulin dosageregimen. Information corresponding to this present insulin dosageregimen is then provided to the patient so that he/she may adjust theamount of insulin they administer.

According to the exemplary embodiment, determination of whether and byhow much to vary a patient's present insulin dosage regimen isundertaken both on the basis of evaluations conducted at predefined timeintervals (every 7 days, for example) as well as asynchronously to suchintervals. The asynchronous determinations will evaluate the patient'sblood-glucose-level data for safety each time a new blood-glucose-levelmeasurement is received to determine whether any urgent action,including any urgent variation to the patient's present insulin dosage,is necessary.

More particularly, each time a new patient blood-glucose-levelmeasurement is received 300 into the memory it is accessed by theprocessor and sorted and tagged according to the time of day themeasurement was received and whether or not it is associated with acertain event, e.g., pre-breakfast, bedtime, nighttime, etc. 310. Onceso sorted and tagged, the new and/or previously recordedblood-glucose-level measurements are subjected to evaluation for theneed to update on the basis of the passage of a predefined period oftime 320 measured by a counter, as well as the need to updateasynchronously for safety 330. For instance, a very low blood glucosemeasurement (e.g., below 50 mg/dL) representing a severe hypoglycemicevent or the accumulation of several low measurements in the past fewdays may lead to an update in the patient's insulin dosage regimenaccording to the step 330, while an update to that regimen may otherwisebe warranted according to the step 320 if a predefined period of time(e.g., 7 days) has elapsed since the patient's insulin dosage regimenwas last updated. In either case, the patient will be provided withinformation 340 corresponding to the present insulin dosage regimen(whether or not it has been changed) to be used in administering his/herinsulin.

Referring next to FIG. 12, there is shown a flowchart that still moreparticularly sets forth an exemplary algorithm by which the inventionmay be implemented to optimize a diabetes patient's insulin dosageregimen. According to the exemplary algorithm, the insulin dosagemodification contemplates separate units of long-acting and short-actinginsulin. However, it will be appreciated that the invention is equallyapplicable to optimize the insulin dosage regimen of a patient wherethat dosage is in another conventional form (such as pre-mixed insulin).It will also be understood from this specification that the inventionmay be implemented otherwise than as particularly described hereinbelow.

According to a first step 400, data corresponding to a patient's newblood-glucose-level measurement is input, such as, for instance, by anyof the exemplary means mentioned above, into the at least first memory(not shown in FIG. 12). This data is accessed and evaluated (by theprocessor) at step 410 of the exemplary algorithm and sorted accordingto the time it was input.

More particularly according to this step 410, the blood-glucose-levelmeasurement data input is “tagged” with an identifier reflective of whenthe reading was input; specifically, whether it is a morning (i.e.,“fast”) measurement (herein “MPG”), a pre-lunch measurement (herein“BPG”), a pre-dinner measurement (herein “LPG”), a bedtime measurement(herein “BTPG”), or a nighttime measurement (herein “NPG”).

The “tagging” process may be facilitated using a clock internal to theprocessor (such as, for instance, the clock of a general purposecomputer) that provides an input time that can be associated with theblood-glucose-level measurement data synchronous to its entry.Alternatively, time data (i.e., “10:00 AM,” “6:00 PM,” etc.) orevent-identifying information (i.e., “lunchtime,” “dinnertime,”“bedtime,” etc.) may be input by the patient reflecting when theblood-glucose-level measurement data was taken, and such informationused to tag the blood-glucose-level measurement data. As a furtheralternative, and according to the embodiment where theblood-glucose-level measurement data are provided directly to the memoryby a glucose monitor, time data may be automatically associated with theblood-glucose-level measurement data by such glucose monitor (forinstance, by using a clock internal to that glucose monitor). It is alsocontemplated that, optionally, the user/patient may be queried (forinstance at a display) for input to confirm or modify any time-tagautomatically assigned a blood-glucose-level measurement data-input.Thus, for instance, a patient may be asked to confirm (via data entrymeans such as, for example, one or more buttons or keys, a touch-screendisplay, etc.) that the most recently input blood-glucose-levelmeasurement data reflects a pre-lunch (BPG) measurement based on thetime stamp associated with the input of the data. If the patientconfirms, then the BPG designation would remain associated with themeasurement. Otherwise, further queries of the patient may be made todetermine the appropriate time designation to associate with themeasurement.

It will be understood that any internal clock used to tag theblood-glucose-level measurement data may, as desired, be user adjustableso as to define the correct time for the time zone where the patient islocated.

Further according to the exemplary embodiment, the various categories(e.g., DPG, MPG, LPG, etc.) into which the blood-glucose-levelmeasurement data are more particularly sorted by the foregoing “tagging”process are as follows:

-   -   NPG—The data are assigned this designation when the time stamp        is between 2 AM and 4 AM.    -   MPG—The data are assigned this designation when the time stamp        is between 4 AM and 10 AM.    -   BPG—The data are assigned this designation when the time stamp        is between 10 AM and 3 PM.    -   LPG—The data are assigned this designation when the time stamp        is between 3 PM and 9 PM.    -   BTPG—The data are assigned this designation when the time stamp        is between 9 PM and 2 AM. If the BTPG data reflect a time more        than three hours after the patient's presumed dinnertime        (according to a predefined time window), then these data are        further categorized as a dinner compensation blood-glucose-level        (herein “DPG”).

According to the employment of a time stamp alone to “tag” theblood-glucose-level data inputs, it will be understood that there existsan underlying assumption that these data were in fact obtained by thepatient within the time-stamp windows specified above.

Per the exemplary embodiment of the invention, if the time stamp of ablood-glucose-level measurement data-input is less than 3 hours from themeasurement that preceded the last meal the patient had, it isconsidered biased and omitted unless it represents a hypoglycemic event.

According to the next step 420, the newly input blood-glucose-levelmeasurement is accessed and evaluated (by the processor) to determine ifthe input reflects a present, severe hypoglycemic event. This evaluationmay be characterized by the exemplary formula PG(t)<w, where PG(t)represents the patient's blood-glucose-level data in mg/dL, and wrepresents a predefined threshold value defining a severe hypoglycemicevent (such as, by way of non-limiting example, 50 mg/dL).

If a severe hypoglycemic event is indicated at step 420 then, accordingto the step 430, the patient's present insulin dosage regimen data (inthe memory 10 [not shown in FIG. 12]) is updated as warranted andindependent of the periodic update evaluation described further below.More particularly, the algorithm will in this step 430 asynchronously(that is, independent of the periodic update evaluation) determinewhether or not to update the patient's insulin dosage regimen on thebasis of whether the patient's input blood-glucose-level data reflectthe accumulation of several low glucose values over a short period oftime. According to the exemplary embodiment, the dosage associated withthe newly input blood-glucose-level measurement is immediatelydecreased. More specifically, for a severe hypoglycemic event at MPG,the long-acting insulin dosage is decreased by 20%; and for a severehypoglycemic event at BPG the breakfast short-acting insulin dose isdecreased by 20%.

The algorithm also at this step 430 updates a counter of hypoglycemicevents to reflect the newly-input (at step 400) blood-glucose-levelmeasurement. Notably, modifications to the patient's insulin dosageregimen according to this step 430 do not reset the timer counting tothe next periodic update evaluation. Thus, variation in the patient'sinsulin dosage regimen according to this step 430 will not prevent thealgorithm from undertaking the next periodic update evaluation.

Any such blood-glucose-level measurement is also entered into ahypoglycemic events database in the memory. In the exemplary embodiment,this is a rolling database that is not reset. Instead, the recordedhypoglycemic events expire from the database after a predefined periodof time has elapsed; essentially, once these data become irrelevant tothe patient's insulin dosage regime. Thus, by way of example only, thisdatabase may contain a record of a hypoglycemic event for 7 days.

Further according to the step 430, one or more warnings may be generatedfor display to the patient (such as via a display 30 [not shown in FIG.12]). Most essentially, it is contemplated that such one or morewarnings would alert a patient to the fact that his/herblood-glucose-level is dangerously low so that appropriate correctivesteps (e.g., ingesting a glucose tablet) could be taken promptly.Additionally, and without limitation, such one or more warnings may alsocorrespond to any one or more of the following determinations:

That the patient's blood-glucose-level measurement data reflect thatthere have been more than two hypoglycemic events during a predeterminedperiod of time (such as, by way of example only, in the past 7 days);that more than two drops in the patient's blood-glucose-levelmeasurements between the nighttime measurement and the morningmeasurement are greater than a predetermined amount in mg/dL (70 mg/dL,for instance); and/or that more than two drops in the patient'sblood-glucose-level measurement between the nighttime measurement andthe morning measurement are greater than a predetermined percentage(such as, for instance, 30%).

If a severe hypoglycemic event is not indicated at step 420, therecorded (in the memory 10) data inputs corresponding to the number ofpatient hypoglycemic events over a predetermined period of days areaccessed and evaluated by the processor (20, not shown) at step 440 todetermine if there have been an excessive number of regular hypoglycemicevents (e.g., a blood-glucose-level measurement between 50 mg/dL and 75mg/dL) over that predetermined period. This evaluation is essentiallydirected to determining whether the patient has experienced an excessivenumber of such regular hypoglycemic events in absolute time andindependent of the periodic update operation as described elsewhereherein. This assessment, made at step 440, may be described by thefollowing, exemplary formula:Is (#{of events at HG}>Q) or is (#{of hypoglycemic events in the last Wdays}=Q)?; where HG represents the recorded number of hypoglycemic events, W is apredefined period of time (e.g., 3 days), and Q is a predefined numberdefining an excessive number of hypoglycemic events (e.g., 3). By way ofexample, Q may equal 3 and W may also equal 3, in which case if it isdetermined in step 440 that there were either 4 recorded hypoglycemicevents or there were 3 recorded hypoglycemic events in the last 3 days,the algorithm proceeds to step 430.

Notably, if step 440 leads to step 430, then a binary (“1” or “0”)hypoglycemic event correction “flag” is set to “1,” meaning that noincrease in the patient's insulin dosage regimen is allowed and thealgorithm jumps to step 490 (the periodic dosage update evaluationroutine). Potentially, the periodic update evaluation may concur thatany or all the parts of the insulin dosage regimen require an increasedue to the nature of blood-glucose-levels currently stored in the memory10 and the execution of the different formulas described hereafter.However, by setting the hypoglycemic event correction flag to “1,” thealgorithm will ignore any such required increase and would leave thesuggested part of the dosage unchanged. Therefore, this will lead to apotential reduction in any or all the components of the insulin dosageregimen to thus address the occurrence of the excessive number ofhypoglycemic events. Further according to this step, the timer countingto the next periodic update evaluation is reset.

In the next step 450, the recorded, time-sorted/taggedblood-glucose-level measurement data corresponding to the number ofpatient hypoglycemic events over a predetermined period of days (forexample, 7 days) are accessed and evaluated by the processor todetermine if there have been an excessive number of such hypoglycemicevents at any one or more of breakfast, lunch, dinner and/or in themorning over the predetermined period. This evaluation may becharacterized by the exemplary formula: #{HG(m)(b)(l)(d) in XX[d]}=Y?;where #HG(m)(b)(l)(d) represents the number of hypoglycemic events atany of the assigned (by the preceding step) measurement times ofmorning, bedtime, lunch or dinner over a period of XX (in the instantexample, 7) days ([d]), and Y represents a number of hypoglycemic eventsthat is predetermined to constitute a threshold sufficient to meritadjustment of the patient's insulin dosage regimen (in the presentexample, 2 hypoglycemic events). It will be appreciated that theemployment of this step in the algorithm permits identification withgreater specificity of possible deficiencies in the patient's presentinsulin dosage regimen. Moreover, the further particularization of whenhypoglycemic events have occurred facilitates time-specific (e.g., afterlunch, at bedtime, etc.) insulin dosage regimen modifications.

If an excessive number of such hypoglycemic events is not indicated atstep 450, then the algorithm queries at step 460 whether or not it istime to update the patient's insulin dosage regimen irrespective of thenon-occurrence of hypoglycemic events, and based instead upon thepassage of a predefined interval of time (e.g., 7 days) since the needto update the patient's insulin dosage regimen was last assessed. Ifsuch an update is not indicated—i.e., because an insufficient timeinterval has passed—then no action is taken with respect to thepatient's insulin dosage and the algorithm ends (indicated by the arrowlabeled “NO”) until the next blood-glucose-level measurement data areinput.

If, however, an update is indicated by the fact that it has been 7 days(or other predefined interval) since the need to update the patient'sinsulin dosage was last evaluated, then before such update is effectedthe algorithm first determines, in step 470, if the patient's generalcondition falls within a predetermined “normal” range. Thisdetermination operation may be characterized by the exemplary formula:xxx≤E{PG}≥yyy; where xxx represents a lower bound for a desiredblood-glucose-level range for the patient, yyy represents an upper boundfor a desired blood-glucose-level range for the patient, and E{PG}represents the mean of the patient's recorded blood-glucose-levelmeasurements. According to the exemplary embodiment, the lower bound xxxmay be predefined as 80 mg/dL, and the upper bound yyy may be predefinedas 135 mg/dL.

It will be understood that the foregoing values may be other than as sospecified, being defined, for instance, according to the particularcountry in which the patient resides. Furthermore, it is contemplatedthat the upper (yyy) and lower (xxx) bounds may be defined by thepatient's healthcare professional, being entered, for instance, via dataentry means such as described elsewhere herein.

Where the patient's general condition is outside of the predetermined“normal” range, the algorithm proceeds to step 490 where the data areevaluated to determine whether it is necessary to correct the patient'slong-acting insulin dosage regimen.

Where, however, the patient's general condition is within thepredetermined “normal” range, the algorithm next (step 480) querieswhether the patient's recorded blood-glucose-level measurement datarepresent a normal (e.g., Gaussian) or abnormal distribution. This maybe characterized by the exemplary formula: −X<E{PG^3}<X; where E{PG^3}represents the third moment of the distribution of the recorded (in thememory) blood-glucose-level measurement data—i.e., the third root of theaverage of the cubed deviations in these data around the mean of therecorded blood-glucose-levels, and X represents a predefined limit(e.g., 5). It is contemplated that the predefined limit X should bereasonably close to 0, thus reflecting that the data (E{PG^3}) are wellbalanced around the mean.

Thus, for example, where X is 5, the data are considered to be normalwhen the third root of the average of the cubed deviations thereofaround the mean of the recorded blood-glucose-levels is greater than −5but less than 5. Otherwise, the data are considered to be abnormal.

Where the data are determined to be normal in step 480 (indicated by thearrow labeled “YES”), then no action is taken to update the patient'sinsulin dosage regimen.

However, if in step 470 the mean of all of a patient's recordedblood-glucose-level measurement data are determined to fall outside ofthe predetermined “normal” range, then in step 490 the algorithmevaluates whether it is necessary to correct the patient's long-actinginsulin dosage regimen. This is done by evaluating whether the patient'srecorded MPG and BTPG data fall within an acceptable range or,alternatively, if there is an indication that the patient's long-actinginsulin dosage should be corrected due to low MPG blood-glucose-levelmeasurements. The determination of whether the patient's MPG and BTPGdata fall within a predetermined range may be characterized by theexemplary formula: xxy≤E{MPG}, E{BTPG}≤yyx; where xxy is a lower boundfor a desired blood-glucose-level range for the patient, yyx is an upperbound for a desired blood-glucose-level range for the patient, E{MPG}represents the mean of the patient's recorded MPG blood-glucose-levelmeasurements, and E{BTPG} represents the mean of the patient's recordedBTPG measurements. According to the exemplary embodiment, xxy may bepredefined as 80 mg/dL, while yyx may be predefined as 200 mg/dL.However, it will be understood that these values may be otherwisepredefined, including, as desired, by the patient's healthcare provider(being entered into the memory via data entry means, for instance).

If the determination in step 490 is positive, then update of thepatient's long-acting insulin dosage (step 510) is bypassed and thealgorithm proceeds to step 500, according to which the patient'sshort-acting insulin dosage (in the form of the carbohydrate ratio(“CHR”), a correction factor δ, and the plasma glucose correction factorare each updated and the hypoglycemic correction “flag” reset to 0 (thuspermitting subsequent modification of the insulin dosage regimen at thenext evaluation thereof).

If, on the other hand, the determination in step 490 is negative, thenthe patient's long-acting insulin dosage is updated at step 510, alongwith performance of the updates specified at step 500. In either case,the process ends following such updates until new patientblood-glucose-level measurement data are input.

Updates of the long-acting insulin dosage regimen data may becharacterized by the following, exemplary formulas:

$\Delta_{up} = {{\left( {1 - {\alpha(2)}} \right){floor}\left\{ \frac{{\alpha(1)}{{LD}(k)}}{100} \right\}} + {{\alpha(2)}{ceil}\left\{ \frac{{\alpha(1)}{{LD}(k)}}{100} \right\}}}$$\Delta_{down} = {{\left( {1 - {\alpha(2)}} \right){floor}\left\{ \frac{{\alpha(1)}{{LD}(k)}}{200} \right\}} + {{\alpha(2)}{ceil}\left\{ \frac{{\alpha(1)}{{LD}(k)}}{200} \right\}}}$  If  E{MPG} < b₁    LD(k + 1) = LD(k) − Δ_(down)   Else   If  E{MPG} > b₂     LD(k + 1) = LD(k) + Δ_(up)   Else  if  E{MPG} > b₃     LD(k + 1) = LD(k) + Δ_(down)    End   End;where α(1) represents a percentage by which the patient's presentlong-acting insulin dosage regimen is to be varied, α(2) represents acorresponding binary value (due to the need to quantize the dosage),LD(k) represents the patient's present dosage of long-acting insulin,LD(k+1) represents the new long-acting insulin dosage, b₁, b₂, and b_(a)represent predetermined blood-glucose-level threshold parameters inmg/dL, and E{MPG} is the mean of the patient's recorded MPGblood-glucose-level measurements.

Since a patient's insulin dosage regimen is expressed in integers (i.e.,units of insulin), it is necessary to decide if a percent change(increase or decrease) in the present dosage regimen of long-actinginsulin that does not equate to an integer value should be the nearesthigher or lower integer. Thus, for instance, if it is necessary toincrease by 20% a patient's long-acting insulin dosage regimen from apresent regimen of 18 units, it is necessary to decide if the new dosageshould be 21 units or 22 units. In the exemplary algorithm, thisdecision is made on the basis of the patient's insulin sensitivity.

Insulin sensitivity is generally defined as the average total number ofinsulin units a patient administer per day divided by the patient weightin kilograms. More particularly, insulin sensitivity (IS(k)) accordingto the exemplary algorithm may be defined as a function of twice thepatient's total daily dosage of long-acting insulin (which may bederived from the recorded data corresponding to the patient's presentinsulin dosage regimen) divided by the patient's weight in kilograms.This is expressed in the following exemplary formula:

${{{IS}(k)} = \frac{2 \cdot {{LD}(k)}}{KK}};$where KK is the patient weight in kilograms.

A patient's insulin sensitivity factor may of course be approximated byother conventional means, including without reliance on entry of datacorresponding to the patient's weight.

More particularly, the inventive algorithm employs an insulinsensitivity correction factor (α_((2×1))(IS)))), a 2 entries vector, todetermine the percentage at which the dosage will be corrected and toeffect an appropriate rounding to the closest whole number for updatesin the patient's CHR, PGR and LD. When the patient's weight is known,this determination may be characterized by the following, exemplaryformula:

${\alpha({IS})} = \left\{ \begin{matrix}{\begin{bmatrix}5 & 0\end{bmatrix}^{\prime},} & {{{IS}(k)} < y_{1}} \\{\begin{bmatrix}10 & 0\end{bmatrix}^{\prime},} & {y_{1} \leq {{IS}(k)} < y_{2}} \\{\begin{bmatrix}20 & 0\end{bmatrix}^{\prime},} & {y_{2} \leq {{IS}(k)} < y_{3}} \\{\begin{bmatrix}20 & 1\end{bmatrix}^{\prime},} & {{y_{3} \leq {{IS}(k)}};}\end{matrix} \right.$where α(1) is a percentage value of adjustment from the present to a newinsulin dosage value, and α(2) is a binary value (i.e., 0 or 1). Thevalue of α(2) is defined by the value of IS(k) in relation to apredefined percent change value (e.g., y₁, y₂, y₃, y₄) for α(1). Thus,in the exemplary embodiment of the algorithm: Where, for example,IS(k)<0.3, the value of α(1) is 5 and the value of α(2) is 0; where0.3≤IS(k)<0.5, the value of α(1) is 10 and the value of α(2) is 0; where0.5≤IS(k)<0.7, the value of α(1) is 20 and the value of α(2) is 0; andwhere 0.7≤IS(k), the value of α(1) is 20 and the value of α(2) is 1.

When the patient weight is unknown, the algorithm will determine a usingthe following alternative: α(2) is set to “1” if the patient long actinginsulin dosage is greater than X units (where, for example X may equal50 insulin units), and the percentage by which we adjust the dosage willbe determined according to the mean of all blood-glucose-levelmeasurements currently in memory (i.e., E{PG}) by:

${\alpha(1)} = \left\{ \begin{matrix}{5,} & {w_{1} \leq {E\left\{ {PG} \right\}} < w_{2}} \\{10,} & {w_{2} \leq {E\left\{ {PG} \right\}} < w_{3}} \\{20,} & {{w_{3} \leq {E\left\{ {PG} \right\}}};}\end{matrix} \right.$where w₁, w₂ and w₃ each represent a predefined blood-glucose-levelexpressed in mg/dL (thus, for example, w₁ may equal 135 mg/dL, w₂ mayequal 200 mg/dL, and w₃ may equal 280 mg/dL).

Returning to the exemplary formulas for updating the patient'slong-acting insulin dosage, in the exemplary algorithm the decision ofwhether and by how much to decrease or increase a patient's long-actinginsulin dosage regimen is based on the predetermined thresholdparameters b₁, b₂, and b₃; where, by way of example only, b₁=80 mg/dL,b₂=120 mg/dL, and b₃=200 mg/dL. More particularly, where the mean of thepatient's MPG blood-glucose-level data is less than 80 mg/dL, the newlong-acting insulin dosage (LD(k+1)) is the present long-acting insulindosage (LD(k)) minus the value of Δ_(down) (which, as shown above, is afunction of the insulin sensitivity correction factors α(1) and α(2),and the patient's long-acting insulin dosage (LD(k)) and may equal halfof Δ_(up)). Otherwise, if the mean of the patient's MPGblood-glucose-level data is greater than 200 mg/dL, the new long-actinginsulin dosage (LD(k+1)) is the present long-acting insulin dosage(LD(k)) plus the value of the Δ_(up) (which, as shown above, is afunction of the insulin sensitivity correction factors α(1) and α(2),and the patient's long-acting insulin dosage (LD(k)). Finally, if themean of the patient's MPG blood-glucose-level data is greater than 150but less than 200, the new long-acting insulin dosage (LD(k+1)) is thepresent long-acting insulin dosage (LD(k)) plus the value of theΔ_(down).

The corrective amount Δ is calculated as a percentage of the currentlong-acting insulin dosage rounded according to α(2). In a particularexample, if α(1)=20, α(2)=0, and the current long acting insulin dosageLD(k)=58, then Δ_(up) equals 20% of 58, which is 11.6, rounded down toΔ_(up)=11. Accordingly, the long-acting insulin dosage would be updatedto LD(k+1)=58+11=69.

It will be appreciated by reference to the foregoing that no “ping-pong”effect is allowed; in other words, the patient's long-acting insulindosage may not be adjustable so that any two successive such adjusteddosages fall below and above the dosage which they immediately succeed.Thus, it is not permitted to have the outcome where the latest LD update(LD(2)) is greater than the initial LD set by the healthcareprofessional (LD(0)), and the preceding LD update (LD(1)) is less thanLD(0). Thus, the outcome LD(2)>LD(0)>LD(1) is not permitted.

Returning to the step 450, if an excessive number of hypoglycemic eventsat any of the time-tagged blood-glucose-level measurement data forbreakfast, lunch, dinner or in the morning over the predetermined period(for instance, 7 days) are indicated from the patient's data, then atstep 520 the algorithm identifies from the recorded, time-tagged data ofhypoglycemic events when those events occurred in order to affect anysubsequently undertaken variation to the patient's insulin dosageregimen, and also sets the binary hypoglycemic correction “flag” (e.g.,“1” or “0”, where 1 represents the occurrence of too many hypoglycemicevents, and 0 represents the nonoccurrence of too many hypoglycemicevents) to 1. The presence of this “flag” in the algorithm at thisjuncture prevents subsequent increases in the patient's insulin dosageregimen in the presence of too many hypoglycemic events.

Further according to this step 520, where the blood-glucose-levelmeasurement data reflects hypoglycemic events in the morning or duringthe night, the algorithm identifies the appropriate modificationrequired to any subsequent variation of the patient's insulin dosageregimen. This may be characterized by the following, exemplary formula:If #HG events in {MPG+NTPG}=X, then reduce LD by α(1)/2; where #HG isthe number of recorded patient hypoglycemic events at the MPG andNTPG-designated blood-glucose-level measurements, X is a predefinedvalue (such as, for example, 2), LD refers to the long-acting insulindosage, and α(1) represents the aforedescribed insulin sensitivitycorrection factor, expressed as a percentage. Thus, α(1)/2 reflects thatthe patient's long-acting insulin dosage is to be reduced only by ½ ofthe value of α(1), if at all, where the recorded hypoglycemic eventsoccur in the morning or overnight.

Further according to this step 520, where the blood-glucose-levelmeasurement data reflects hypoglycemic events during the day, thealgorithm identifies the appropriate modification required to anysubsequent variation of the patient's insulin dosage regimen. This maybe characterized by the following formula: If #HG events in {BPG or LPGor NTPG}=X, then see update δ; where #HG is the number of recordedpatient hypoglycemic events at any of the BPG, LPG or NTPG time-taggedmeasurements, X is a predefined value (for instance, 2), and “see updateδ” refers to short-acting insulin dosage correction factor δincorporated into the exemplary form of the algorithm, as describedherein.

Following step 520, the algorithm queries 530 whether it is time toupdate the patient's insulin dosage regimen irrespective of theoccurrence of hypoglycemic events and based upon the passage of apredefined interval of time (by way of non-limiting example, 7 days)since the need to update the patient's insulin dosage regimen was lastassessed. Thus, it is possible that a patient's insulin dosage regimenwill not be updated even though the HG correction flag has been“tripped” (indicating the occurrence of too many hypoglycemic events) ifan insufficient period of time has passed since the regimen was lastupdated.

If an insufficient period of time has passed, the process is at an end(indicated by the arrow labeled “NO”) until new blood-glucose-levelmeasurement data are input. If, on the other hand, the predefined periodof time has passed, then the algorithm proceeds to the step 490 todetermine if the long-acting insulin dosage has to be updated asdescribed before followed by the update step 500, according to which thepatient's short-acting insulin dosage (in the form of the carbohydrateratio (“CHR”)), the correction factor δ, and plasma glucose correctionfactor are each updated and the hypoglycemic correction flag reset to 0.

According to the step 500, an update to the patient's plasma glucosecorrection factor (“PGR”) is undertaken. This may be characterized bythe following, exemplary formulas:

${{{Calculate}\mspace{14mu}{new}\mspace{14mu}{PGR}\mspace{14mu}\left( {{}_{}^{}{}_{}^{}} \right)\text{:}\mspace{14mu}{NPGR}} = {{\frac{1700}{E\left\{ {DT} \right\}}.{Calculate}}\mspace{14mu}{difference}}},{\Delta = {{{{PGR}(k)} - {NPGR}}}}$${{If}\mspace{14mu}\frac{\Delta}{{PGR}(k)}} \leq \frac{\alpha(1)}{100}$Δ = (1 − α(2))floor{Δ} + α(2)ceil{Δ} Else$\Delta = {{\left( {1 - {\alpha(2)}} \right){floor}\left\{ \frac{{\alpha(1)}{{PGR}(k)}}{100} \right\}} + {{\alpha(2)}{ceil}\left\{ \frac{{\alpha(1)}{{PGR}(k)}}{100} \right\}}}$End PGR(k + 1) = PGR(k) + Δ ⋅ sign(NPGR − PGR(k))PGR(k + 1) = quant(PGR(k + 1), ZZ); Quantize  correction  to  steps  of  ZZ[mg/dL].

More particularly, the new PGR (“NPGR”) is a function of a predefinedvalue (e.g., 1700) divided by twice the patient's total daily dosage oflong-acting insulin in the present insulin dosage regimen. In theforegoing formulas, the value of this divisor is represented by E{DT},since the value representing twice the patient's daily dosage oflong-acting insulin in the present insulin dosage regimen is substitutedas an approximation for the mean of the total daily dosage of insulinadministered to the patient (which data may, optionally, be employed ifthey are input into the memory by an insulin pump, such as in theexemplary apparatus described above, or by the patient using data entrymeans). The resultant value is subtracted from the present patient PGR(“PGR(k)”) to define a difference (“Δ”). If the Δ divided by the presentPGR(k) is less than or equal to the value of α(1) divided by 100, thenthe integer value of Δ (by which new PGR (i.e., PGR(k+1)) is updated) isa function of the formula Δ=(1−α(2))floor{Δ}+α(2)ceil{Δ}, where α(2) isthe insulin sensitivity correction factor (1 or 0), “floor” is value ofΔ rounded down to the next integer, and “ceil” is the value of Δ roundedup to the next integer. If, on the other hand, the Δ divided by thepresent PGR(k) is greater than the value of α(1) divided by 100, thenthe integer value of Δ is a function of the formula

${\Delta = {{\left( {1 - {\alpha(2)}} \right)\;{floor}\;\left\{ \frac{{\alpha(1)}{{PGR}(k)}}{100} \right\}} + {{\alpha(2)}{ceil}\left\{ \frac{{\alpha(1)}{{PGR}(k)}}{100} \right\}}}},$where α(2) is the insulin sensitivity correction factor (1 or 0), α(1)is the percent value of the insulin sensitivity correction factor,PGR(k) is the present PGR, “floor” is value of Δ rounded down to thenext integer, and “ceil” is the value of Δ rounded up to the nextinteger. According to either outcome, the new PGR (PGR(k+1)) is equal tothe present PGR (PGR(k)) plus Δ times the sign of the difference,positive or negative, of NPGR minus PGR(k).

Furthermore, it is contemplated that the new PGR will be quantized topredefined steps of mg/dL. This is represented by the exemplary formula:PGR(k+1)=quant(PGR(k+1), ZZ) PGR(k+1)=quant(PGR(k+1), ZZ); where, by wayof a non-limiting example, ZZ may equal 5.

Also according to the update step 500, updates to the patient'sshort-acting insulin dosage regimen are undertaken by modifying thecarbohydrate ratio (CHR). CHR represents the average carbohydrate toinsulin ratio that a patient needs to determine the correct dose ofinsulin to inject before each meal. This process may be characterized bythe following, exemplary formulas:

${{Calculate}\mspace{14mu}{new}\mspace{14mu}{CHR}\mspace{14mu}\left( {{}_{}^{}{}_{}^{}} \right)},\;{{NCHR} = \frac{500}{E\left\{ {DT} \right\}}}$Calculate  difference, Δ = CHR(k) − NCHR${{If}\mspace{14mu}\frac{\Delta}{{CHR}(k)}} \leq \frac{\alpha(1)}{100}$  Δ = (1 − α(2))floor{Δ} + α(2)ceil{Δ} Else$\mspace{20mu}{\Delta = {{\left( {1 - {\alpha(2)}} \right){floor}\left\{ \frac{{\alpha(1)}{{CHR}(k)}}{100} \right\}} + {{\alpha(2)}{ceil}\left\{ \frac{{\alpha(1)}{{CHR}(k)}}{100} \right\}}}}$End CHR(k + 1) = CHR(k) + Δ ⋅ sign(NCHR − CHR(k))

More particularly, the new CHR (“NCHR”) is a function of a predefinedvalue (e.g., 500) divided by twice the patient's total daily dosage oflong-acting insulin in the present insulin dosage regimen. In theforegoing formulas, the value of this divisor is represented by E{DT},since the value representing twice the patient's daily dosage oflong-acting insulin in the present insulin dosage regimen is substitutedas an approximation for the mean of the total daily dosage of insulinadministered to the patient (which data may, optionally, be employed ifthey are input into the memory by an insulin pump, such as in theexemplary apparatus described above, or by the patient using data entrymeans). The resultant value is subtracted from the present patient CHR(“CHR(k)”) to define a difference (“Δ”). If the Δ divided by the presentCHR(k) is less than or equal to the value of α(1) divided by 100, thenthe integer value of Δ (by which new CHR (i.e., CHR(k+1)) is updated) isa function of the formula Δ=(1−α(2))floor{Δ}+α(2)ceil{Δ}, where α(2) isthe insulin sensitivity correction factor (1 or 0), “floor” is value ofΔ rounded down to the next integer, and “ceil” is the value of Δ roundedup to the next integer. If, on the other hand, the Δ divided by thepresent CHR(k) is greater than the value of α(1) divided by 100, thenthe integer value of Δ is a function of the formula

${\Delta = {{\left( {1 - {\alpha(2)}} \right)\;{floor}\;\left\{ \frac{{\alpha(1)}{{CHR}(k)}}{100} \right\}} + {{\alpha(2)}{ceil}\left\{ \frac{{\alpha(1)}{{CHR}(k)}}{100} \right\}}}},$where α(2) is the insulin sensitivity correction factor (1 or 0), α(1)is the percent value of the insulin sensitivity correction factor,CHR(k) is the present CHR, “floor” is value of Δ rounded down to thenext integer, and “ceil” is the value of Δ rounded up to the nextinteger. According to either outcome, the new CHR (CHR(k+1)) is equal tothe present CHR (CHR(k)) plus Δ times the sign of the difference,positive or negative, of NCHR minus CHR(k).

As patients may respond differently to doses of short-acting insulindepending upon the time of day the injection is made, a different doseof insulin may be required to compensate for a similar amount ofcarbohydrates consumed for breakfast, lunch, or dinner. For example, onemay administer ‘1’ insulin unit for every ‘10’ grams of carbohydratesconsumed at lunch while administering ‘1’ insulin unit for every ‘8’grams of carbohydrates consumed at dinner. In the exemplary embodimentof the algorithm, this flexibility is achieved by the parameter Delta,δ, which is also updated in the step 500. It will be understood that thecarbohydrate to insulin ratio (CHR) as calculated above is the same forall meals. However, the actual dosage differs among meals (i.e.,breakfast, lunch, dinner) and equals CHR-δ. Therefore, the exemplaryalgorithm allows the dosage to be made more effective by slightlyaltering the CHR with δ to compensate for a patient's individualresponse to insulin at different times of the day.

Delta δ is a set of integers representing grams of carbohydrates, and ismore specifically defined as the set of values [δb, δl, δd], where “b”represents breakfast, “l” represents lunch, and “d” represents dinner.Delta, δ, may be either positive—thus reflecting that before a certainmeal it is desired to increase the insulin dose—or negative—thusreflecting that due to hypoglycemic events during the day it is desiredto decrease the insulin dose for a given meal.

Initially, it is contemplated that each δ in the set [δb, δl, δd] may bedefined by the patient's healthcare professional or constitute apredefined value (e.g., δ=[0, 0, 0] for each of [b, l, d], or [0b, 0l,0d], thus reflecting that the patient's CHR is used with no alterationfor breakfast, lunch, or dinner).

The range of δ (“Rδ”) is defined as the maximum of three differences,expressed as max(|δb−δl|, |δb−δd|, |δd−δl|). In addition the algorithmdefines the minimal entry (“δ_(min)”) of the set [δb, δl, δd], expressedas min(δb, δl, δd).

Any correction to the patient's CHR can only result in a new Rδ(“Rδ(k+1)”) that is less than or equal to the greatest of the range ofthe present set of δ (Rδ(k)) or a predefined limit (D), which may, forinstance, be 2, as in the exemplary embodiment.

Against the foregoing, if the number of hypoglycemic events (HG) in agiven meal (b, l or d) over a predefined period (for example, 7 days) isequal to a predefined value (for instance, 2), and if the correspondingδb, δl, or δd is not equal to the δ_(min) or the range is 0 (R_(δ)=0),then the decrease in that δ (δb, δl, or δd) is equal to the presentvalue for that δ minus a predefined value (“d”), which may, forinstance, be 1; thus, δ_({l})=δ_({l})−d.

Otherwise, if the corresponding δb, δl, or δd is equal to the δ_(min)and the range is other than 0, then the decrease in that δ (e.g., δb,δl, or δd) is effected by decreasing each δ in the set (i.e., [δb, δl,or δd]) by the predefined value “d” (e.g., 1); thus, δ=δ−d (where δrefers to the entire set [δb, δl, or δd]).

If, on the other hand, the number of hypoglycemic events stored in thememory is insignificant, it may be necessary to increase δ in one ormore of the set (i.e., [δb, δl, or δd]). To determine if an increase isdue, the algorithm looks for an unbalanced response to insulin betweenthe three meals (b, l, d). A patient's response to his/her recentshort-acting insulin dosage is considered unbalanced if the meanblood-glucose-level measurements associated with two of the three mealsfalls within a predefined acceptable range (e.g., >α₁ but <α₂; where,for instance, α₁=80 and α₂=120), while the mean of theblood-glucose-level measurements associated with the third meal fallsabove the predefined acceptable range.

If the mean for two meals falls within [α₁, α₂], while the mean of thethird meal is >α₂, then the δ values for the updated set [δb, δl, or δd]are defined by the following, exemplary formulas:δ_(tmp)=δ;δ_(tmp)(i)=δ_(tmp)(i)+d;If (R _(δ-tmp) <=R _(δ)) or (R _(δ-tmp) <=D), then δ=δ_(tmp)According to the foregoing, a test set of [δb, δl, or δd], designatedδ_(tmp), is defined, wherein the value of each of δb, δl, and δd equalsthe present value of each corresponding δb, δl, and δd. The δ value inthe test set corresponding to the meal (b, l, or d) where theblood-glucose-level measurement was determined to exceed the predefinedacceptable range (e.g., >α₂) is then increased by the value “d” (e.g.,1), and the new set is accepted if it complies with one of thestatements: R_(δ-tmp)<=R_(δ) (i.e., is the range R_(δ) of the test set(“R_(δ-tmp)”) less than or equal to the range (R_(δ)) of the presentset; or R_(δ-tmp)<=D (i.e., is the range R_(δ) of the test set(“R_(δ-tmp)”) less than or equal to the predefined value “D” (e.g., 2).

The foregoing will thus yield an increase in the insulin dosage for aparticular meal if the patient's mean blood-glucose-level measurementdata are outside of a predetermined range, such as, by way of exampleonly, between α₁=80 and α₂=120.

Further according to this step 500, the binary hypoglycemiccorrection-flag is reset to 0, reflecting that the patient's insulindosage regimen has been updated (and thus may be updated again at thenext evaluation).

It will be appreciated that the PGR and CHR values determined at step500 may optionally be employed by the processor to calculate, perconventional formulas, a “sliding scale”-type insulin dosage regimen.Such calculations may employ as a basis therefor a predefined averagenumber of carbohydrates for each meal. Alternatively, data correspondingto such information may be input into the memory by the patient usingdata entry means.

Per the exemplary algorithm as described above, it will be appreciatedthat if a hypoglycemic event causes some dosage reduction, no otherdosage can go up at the next update cycle.

It should be noted that, according to the exemplary embodiment of thealgorithm herein described, any time a periodic evaluation of thepatient insulin dosage regimen is undertaken, the algorithm treats theinsulin dosage regimen as having been updated even if there has been nochange made to the immediately preceding insulin dosage regimen. And,moreover, any time the insulin dosage regimen is updated, whether inconsequence of a periodic update evaluation or an asynchronous update,the timer counting to the next periodic update evaluation will be resetto zero.

As noted, in operation of the invention according to any of the severalembodiments as described herein there is initially specified by ahealthcare professional a patient insulin dosage regimen comprised of,for example, a long-acting insulin dose component, a carbohydrate ratiocomponent and a plasma-glucose correction factor component. This insulindosage regimen data is entered in the memory of an apparatus, such as bya healthcare professional, in the first instance and before the patienthas made any use of the apparatus. Optionally, and as necessary, theinternal clock of the apparatus is set for the correct time for the timezone where the patient resides so that the time tags assigned topatient's blood-glucose-level measurements as they are subsequentlyinput into the apparatus are accurate in relation to when, in fact, thedata are input (whether automatically, manually, or a combination ofboth). Thereafter, the patient will input, or there will otherwiseautomatically be input (such as by the glucose meter) into the memory atleast data corresponding to each successive one of the patient'sblood-glucose-level measurements. Upon the input of such data, theprocessor determines, such as via the algorithm described hereinabove,whether and by how much to vary the patient's present insulin dosageregimen. Information corresponding to this present insulin dosageregimen is then provided to the patient so that he/she may adjust theamount of insulin they administer.

The foregoing description of the exemplary embodiments of the inventionhas been presented for purposes of illustration and description. Theyare not intended to be exhaustive of, or to limit the invention to, theprecise forms disclosed, and modifications and variations thereof arepossible in light of the above teachings or may be acquired frompractice of the invention. The illustrated embodiments are shown anddescribed in order to explain the principals of the innovation and itspractical application to enable one skilled in the art to utilize theinnovation in these and various additional embodiments and with variousmodifications as are suited to the particular use contemplated. Althoughonly a few exemplary embodiments of the present innovations have beendescribed in detail in this disclosure, those skilled in the art whoreview this disclosure will readily appreciate that many modificationsare possible without materially departing from the novel teachings andadvantages of the subject matter herein recited. Accordingly, all suchmodifications are intended to be included within the scope of thepresent innovations. Other substitutions, modifications, changes andomissions may be made in the design, operating conditions andarrangement of the exemplary embodiments without departing from thespirit of the present invention.

The invention in which an exclusive property or privilege is claimed isdefined as follows:
 1. A system for optimizing a patient's insulindosage regimen over time, comprising: at least a first memory forstoring data inputs corresponding at least to one or more components ofa patient's present insulin dosage regimen, and data inputscorresponding at least to the patient's blood-glucose-level measurementsdetermined at a plurality of times; a timer to monitor a predeterminedtime period; a processor operatively connected to the at least firstmemory, the processor programmed to increment the timer based on atleast one of the passage of a predetermined increment of time and thereceipt of at least one of the plurality of blood glucose-levelmeasurements and at the end of the predetermined time period, determinefrom the data inputs corresponding to the patient's blood-glucose-levelmeasurements determined at a plurality of times within the predeterminedtime interval, whether and by how much to vary at least one of the oneor more components in the patient's present insulin dosage regimen inorder to maintain the patient's future blood-glucose-level measurementswithin a predefined range, wherein the timer is reinitiated after thedetermination of whether and by how much to vary at least one of the oneor more components in the patient's present insulin dosage regimen; anda display configured to receive and display the revised at least one ofthe one or more components in the patients insulin dosage regimen;wherein the predetermined time interval is longer than a time periodbetween any two consecutive blood-glucose-level measurements, whereinthe processor is programmed to determine, prior to receiving asubsequent data input, whether the current data input corresponding tothe patient's blood-glucose-level measurements represents a severehypoglycemic event, and to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen in responseto a determination that a data input corresponding to the patient'sblood-glucose-level measurements represents a severe hypoglycemic event;and wherein the processor is further programmed to determine from thedata inputs corresponding to the patient's blood-glucose-levelmeasurements determined at a plurality of times if there have been anexcessive number of hypoglycemic events over the predetermined timeperiod, and to vary at least one of the one or more components in thepatient's present insulin dosage regimen in response to a determinationthat there have been an excessive number of such hypoglycemic eventsover the predetermined time period.
 2. The system of claim 1, whereinthe at least first memory and the processor are resident in a singleapparatus.
 3. The system of claim 2, wherein the single apparatusfurther comprises a glucose meter.
 4. The system of claim 2, furthercomprising a glucose meter that is separate from the single apparatus,the glucose meter adapted to communicate to the at least first memory ofthe single apparatus the data inputs corresponding at least to thepatient's blood-glucose-level measurements determined at a plurality oftimes.
 5. The system of claim 2, wherein the single apparatus furthercomprises data entry means for entering data inputs corresponding atleast to the patient's blood-glucose-level measurements determined at aplurality of times directly into the at least first memory.
 6. Thesystem of claim 5, further comprising data entry means disposed at alocation remote from the single apparatus for remotely entering datainputs corresponding at least to the one or more components in thepatient's present insulin dosage regimen into the at least first memory.7. The system of claim 1, further comprising at least first data entrymeans disposed at a location remote from the at least first memory andprocessor for remotely entering data inputs corresponding at least tothe one or more components in the patient's present insulin dosageregimen into the at least first memory, and at least second data entrymeans, disposed at a location remote from the at least first memory,processor and at least first data entry means, for remotely enteringdata inputs corresponding at least to the patient's blood-glucose-levelmeasurements determined at a plurality of times into the at least firstmemory.
 8. The system of claim 1, wherein the data inputs correspondingat least to the patient's blood-glucose-level measurements determined ata plurality of times are each associated with an identifier indicativeof when the measurement was input into the memory.
 9. The system ofclaim 8, further comprising data entry means enabling a user to definethe identifier associated with each blood-glucose-level measurementdata-input.
 10. The system of claim 8, further comprising data entrymeans enabling a user to confirm the correctness of the identifierassociated with each blood-glucose-level measurement data-input.
 11. Thesystem of claim 8, further comprising data entry means enabling a userto modify the identifier associated with each blood-glucose-levelmeasurement data-input.
 12. The system of claim 1, wherein the processoris programmed to determine on a predefined schedule whether and by howmuch to vary at least one of the one or more components in the patient'spresent insulin dosage regimen.
 13. The system of claim 1, wherein theprocessor is programmed to determine from the data inputs correspondingat least to the patient's blood-glucose-level measurements determined ata plurality of times if the patient's blood-glucose level measurementsfall within or outside of a predefined range, and to vary at least oneof the one or more components in the patient's present insulin dosageregimen only if the patient's blood-glucose level measurements falloutside of the predefined range.
 14. The system of claim 13, wherein theprocessor is further programmed to determine from the data inputscorresponding at least to the patient's blood-glucose-level measurementsdetermined at a plurality of times whether the patient'sblood-glucose-level measurements determined at a plurality of timesrepresent a normal or abnormal distribution.
 15. The system of claim 14,wherein the determination of whether the patient's blood-glucose-levelmeasurements determined at a plurality of times represent a normal orabnormal distribution comprises determining whether the third moment ofthe distribution of the patient's blood-glucose-level measurementsdetermined at a plurality of times fall within a predefined range. 16.The system of claim 8, wherein the one or more components in thepatient's present insulin dosage regimen comprise a long-acting insulindosage component, and wherein the processor is programmed to determinefrom the identifier indicative of when a measurement was input into thememory at least whether the measurement is a morning or bed-timeblood-glucose-level measurement, to determine whether the patient'smorning and bed-time blood-glucose-level measurements fall within apredefined range, and to determine by how much to vary the patient'slong-acting insulin dosage component only when the patient's morning andbed-time blood-glucose-level measurements are determined to fall outsideof the said predefined range.
 17. The system of claim 16, wherein, inconnection with the determination of by how much to vary at least one ofthe one or more components in the patient's present insulin dosageregimen, the processor is programmed to factor in an insulin sensitivitycorrection factor that defines both the percentage by which any of theone or more components of the insulin dosage regimen may be varied andthe direction in which any fractional variations in any of the one ormore components are rounded to the nearest whole number.
 18. The systemof claim 17, wherein the at least first memory further stores datainputs corresponding to a patient's present weight, and wherein theinsulin sensitivity correction factor is in part determined from thepatient's present weight.
 19. The system of claim 17, wherein thedetermination of by how much to vary the long-acting insulin dosagecomponent of a patient's present insulin dosage regimen is a function ofthe present long-acting insulin dosage, the insulin sensitivitycorrection factor, and the patient's blood-glucose-level measurements.20. The system of claim 1, wherein the one or more components in thepatient's present insulin dosage regimen comprise a short-acting insulindosage component defined by a carbohydrate ratio and plasma glucosecorrection factor, and wherein the processor is programmed to determinewhether and by how much to vary the patient's carbohydrate ratio andplasma glucose correction factor.
 21. The system of claim 20, wherein,in connection with the determination of by how much to vary at least oneof the one or more components in the patient's present insulin dosageregimen, the processor is programmed to factor in an insulin sensitivitycorrection factor that defines both the percentage by which any one ormore components of the insulin dosage regimen may be varied and thedirection in which any fractional variations in the one or morecomponents are rounded to the nearest whole number.
 22. The system ofclaim 21, wherein the determination of by how much to vary the presentplasma glucose correction factor component of a patient's insulin dosageregimen is a function of a predefined value divided by the mean of thetotal daily dosage of insulin administered to the patient, the patient'spresent plasma glucose correction factor, and the insulin sensitivitycorrection factor.
 23. The system of claim 22, wherein a valuerepresenting twice the patient's daily dosage of long-acting insulin inthe present insulin dosage regimen is substituted for the mean of thetotal daily dosage of insulin administered to the patient as anapproximation thereof.
 24. The system of claim 22, wherein the plasmaglucose correction factor component of the patient's insulin dosageregimen is quantized to predefined steps of mg/dL.
 25. The system ofclaim 21, wherein the determination of by how much to vary the presentcarbohydrate ratio component of a patient's insulin dosage regimen is afunction of a predefined value divided by the mean of the total dailydosage of insulin administered to the patient, the patient's presentcarbohydrate ratio, and the insulin sensitivity correction factor. 26.The system of claim 25, wherein a value representing twice the patient'sdaily dosage of long-acting insulin in the present insulin dosageregimen is substituted for the mean of the total daily dosage of insulinadministered to the patient as an approximation thereof.
 27. The systemof claim 25, wherein the processor is programmed to determine acorrection factor that allows variations to the carbohydrate ratiocomponent of a patient's insulin dosage regimen to be altered in orderto compensate for a patient's individual response to insulin atdifferent times of the day.
 28. The system of claim 1, wherein the oneor more components in the patient's present insulin dosage regimencomprise a long-acting insulin dosage component, and the determinationof by how much to vary the long-acting insulin dosage component isconstrained to an amount of variation within predefined limits.
 29. Thesystem of claim 1, wherein the one or more components in the patient'spresent insulin dosage regimen comprise a short-acting insulin dosagecomponent defined by a carbohydrate ratio and plasma glucose correctionfactor, and the determination of by how much to vary any one or more ofeach component in the short-acting insulin dosage is constrained to anamount of variation within predefined limits.
 30. The system of claim 1,wherein the one or more components in the patient's present insulindosage regimen comprise a short-acting insulin dosage component takenaccording to a sliding scale, and wherein the processor is programmed todetermine whether and by how much to vary at least the sliding scale inorder to maintain the patient's future blood-glucose-level measurementswithin a predefined range.
 31. The system of claim 30, wherein thedetermination of by how much to vary the sliding scale is constrained toan amount of variation within predefined limits.